Local Regularity Analysis with Wavelet Transform in Gear Tooth Failure Detection

Nissilä, Juhani

doi:10.1515/mspe-2017-0026

Cited by 3 publications

(5 citation statements)

References 4 publications

(7 reference statements)

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“…The number of local irregularities increases even more when the misalignment is 0.50 mm (figures [19][20][21]. We now clearly see that the local irregularities occur roughly in bursts that recur four times per revolution of the axis.…”

Section: Calculations and Discussionmentioning

confidence: 79%

“…Although it is possible that such behaviour or something very close to it may exist in real vibration measurements as well, we will assume here that all the irregularities in our measured vibrations are isolated. The irregularities will thus be found using the following algorithm, which is a modified version of the ones used in [5] and [19].…”

Section: Hölder Regularity and Wavelet Modulus Maximamentioning

confidence: 99%

“…In [19], the first author of this paper studied acceleration measurements of the epicyclic gearboxes of a hydroelectric power station. The main observation was that the vibrations caused by the higher speed gearbox were more irregular.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosing simultaneous faults using the local regularity of vibration signals

Nissilä¹,

Laurila²

2019

Meas. Sci. Technol.

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The regularity of the vibration signals measured from a rotating machine is often affected by the condition of the machine. The fractional order of regularity can be measured using the definition of Hölder continuity. In this paper, we review the connection between the pointwise Hölder regularity of a signal and its wavelet transform. We calculate the wavelet transform modulus of acceleration measurements from a test rig. The effects of different faults were recorded, such as unbalance, the coupling misalignment of a claw clutch, the absence of lubrication in a ball bearing, the absence of the bearing's cage, and their combinations. An analysis of the estimated isolated pointwise regularities from the wavelet transform modulus maxima ridges shows that the faults often cause irregularities in the signals and that their locations and frequencies can be used in diagnosing the faults. Coupling misalignment and the absence of lubrication in a ball bearing both cause impact-like vibrations, but these impacts have positive and negative regularities in the case of a coupling misalignment and mainly negative in the case of a dry bearing. Unbalance is best diagnosed from the integrals of the acceleration signals using traditional methods. In diagnosing the misalignment, bearing problems and simultaneous faults, the local regularity analysis outperforms the use of high order norms of differentiated acceleration measurements (i.e. jerk and snap signals). Using just three features (the number of local irregularities in an acceleration signal, their mean Hölder regularity and the arithmetic mean of the absolute values of a velocity signal), a quadratic classifier can be constructed whose estimated classification error is only 0.3 %.

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Section: Calculations and Discussionmentioning

confidence: 79%

Section: Hölder Regularity and Wavelet Modulus Maximamentioning

confidence: 99%

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Diagnosing simultaneous faults using the local regularity of vibration signals

Nissilä¹,

Laurila²

2019

Meas. Sci. Technol.

Self Cite

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“…These include Yu J [75], who diagnosed the fault in gear shafts, and Yang [76], who diagnosed gearbox faults in wind turbines. Some other researchers [77][78][79][80] proposed new analysis methods for the early detection of gear faults in multi-stage gearboxes based on wavelet analysis.…”

Section: Wavelet Analysis For Gearbox Diagnosticsmentioning

confidence: 99%

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Nguyen,

Nguyen

2024

Applied Sciences

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Wavelet transform is a powerful time-frequency-based analysis method often used in gear fault diagnostics. The development of wavelet transform is closely linked to the improvement of resolution. When the high-frequency resolution allows for easy observation of different frequency components, it is a symptom of damage to an individual part of the machine. This study effectively applied the Wavelet analysis technique to diagnose faulty gearboxes operated in non-stationary conditions. This is a complex problem that usual diagnostic approaches need help to solve due to its non-linear character. This work conducted a simulation and real-world testing to show that the newest wavelet analysis techniques work well, showing that they can accurately find gear faults in gearboxes in non-stationary conditions. A thorough overview of the cutting-edge applications of wavelet transform in diagnosing faults in industrial gearbox systems was also given. This work also explained in detail the mathematical ideas behind the continuous wavelet transform, discrete wavelet transforms, and wavelet packet transform.

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“…A wavelet transformation selects the frequency band with the maximum kurtosis to filter. However, it takes a frequency range of the signal and discards the rest of the information [ 7 ]. The measured vibration signals are considered as a convolution process of the malfunction inducing periodic impact signals and a resonant response of the mechanical components.…”

Section: Introductionmentioning

confidence: 99%

Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and Permutation Entropy

Sun

Liang

et al. 2018

Entropy

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The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and the permutation entropy was proposed to solve this problem in the present paper. The complexity of the periodic impact signal was low and the permutation entropy was relatively small. Moreover, the amplitude of the impact was relatively large. Based on these advantages, the multipoint reciprocal permutation entropy (MRPE) was proposed to track the impact fault source of the weak fault feature in gearbox compound faults. The impact fault period was indicated through MRPE. MOMEDA achieved signal denoising. The optimal filter coefficients were solved using MOMEDA. It exhibits an outstanding performance for noise suppression of gearbox signals with a periodic impact. The results from the transmission show that the proposed method can identify multiple faults simultaneously on a driving gear in the 4th gear of the transmission.

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Local Regularity Analysis with Wavelet Transform in Gear Tooth Failure Detection

Cited by 3 publications

References 4 publications

Diagnosing simultaneous faults using the local regularity of vibration signals

Diagnosing simultaneous faults using the local regularity of vibration signals

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and Permutation Entropy

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